MIMO technology is one of the key technologies for enabling high throughput wireless communication. MIMO systems employ multiple antennas at both ends of a wireless link and can increase data rate by transmitting multiple data streams concurrently and in the same frequency band. Consequently, many wireless communication standards, for example, IEEE 802.11ac, IEEE 802.16e, and 3GPP LTE may take advantage of MIMO technology. Unfortunately, the considerable throughput improvements entail a significant increase in signal processing complexity to detect the transmitted signal with low probability of error at the receiver. This process of estimating the transmitted signal is usually termed as MIMO detector or decoder.
QR decomposition is one of the key techniques for MIMO receivers, since numerous MIMO detection algorithms require the QR decomposition of the channel matrix as starting point. The main purpose of the QR decomposition technique is to factorize a complex channel matrix as a product of an orthogonal matrix and an upper triangular matrix. The various MIMO decoders like linear detection by back substitution, successive interference cancellation (SIC), and tree-search-based algorithms such as the maximum-likelihood performance-achieving sphere decoder use the QR decomposition technique. Hence, to meet the demands of all required MIMO algorithms, a highly efficient low complexity QR decomposition module is needed.
A known technique of MIMO decoder method by combining QR based Zero Forcing (ZF) technique with successive interference cancellation and a reduced Maximum Likelihood (ML) search to obtain near V-BLAST decoding performance requires high number of division operations. Such a large number of the division operations make the known QR decomposition techniques impractical in real time for high-dimensional MIMO systems.
Broadly, three known techniques are widely used to achieve QR decomposition: a Gram-Schmidt technique, a Householder transformation technique, and a Givens rotation technique. The Gram-Schmidt technique obtains an orthogonal basis spanning column space of a matrix to be decomposed. Meanwhile, an orthogonality principle is utilized to derive the upper triangular matrix. The Householder transformation technique tries to zero out the elements below the diagonal matrix of each column vector at a stroke by reflection operations to get the upper triangular matrix. On the contrary, the Givens rotation technique zeros one element of the matrix at a time by two-dimensional rotation. An implementation of the Gram-Schmidt, Householder transformation, and Givens rotation techniques require multiplication, division and square-root operations, resulting in high hardware complexity and computation latency.
A technique using modified sequence of Givens rotations algorithm is also known, however, this technique still needs a large number of rotation and division operations for high-dimensional MIMO systems.
Therefore, there exists a need for a reduced complexity MIMO decoding technique.